1 code implementation • CVPR 2018 • Jae-Han Lee, Minhyeok Heo, Kyung-Rae Kim, Chang-Su Kim
We propose a deep learning algorithm for single-image depth estimation based on the Fourier frequency domain analysis.
no code implementations • ECCV 2018 • Minhyeok Heo, Jae-Han Lee, Kyung-Rae Kim, Han-Ul Kim, Chang-Su Kim
We propose a monocular depth estimation algorithm, which extracts a depth map from a single image, based on whole strip masking (WSM) and reliability-based refinement.
no code implementations • CVPR 2019 • Jae-Han Lee, Chang-Su Kim
We propose a novel algorithm for monocular depth estimation using relative depth maps.
Ranked #63 on Monocular Depth Estimation on NYU-Depth V2
1 code implementation • ICCV 2021 • Jae-Han Lee, Chul Lee, Chang-Su Kim
We propose a novel loss weighting algorithm, called loss scale balancing (LSB), for multi-task learning (MTL) of pixelwise vision tasks.
1 code implementation • CVPR 2022 • Jae-Han Lee, Seungmin Jeon, Kwang Pyo Choi, Youngo Park, Chang-Su Kim
We propose the deep progressive image compression using trit-planes (DPICT) algorithm, which is the first learning-based codec supporting fine granular scalability (FGS).
no code implementations • 25 Mar 2022 • Seungmin Jeon, Jae-Han Lee, Chang-Su Kim
DPICT is the first learning-based image codec supporting fine granular scalability.
1 code implementation • 23 Aug 2022 • Jinyoung Jun, Jae-Han Lee, Chul Lee, Chang-Su Kim
We propose a novel algorithm for monocular depth estimation that decomposes a metric depth map into a normalized depth map and scale features.
Ranked #37 on Monocular Depth Estimation on NYU-Depth V2 (using extra training data)
1 code implementation • ICCV 2023 • Jaehyeok Bae, Jae-Han Lee, Seyun Kim
Because anomalous samples cannot be used for training, many anomaly detection and localization methods use pre-trained networks and non-parametric modeling to estimate encoded feature distribution.
Ranked #4 on Anomaly Detection on BTAD (using extra training data)
no code implementations • 20 Mar 2023 • Jinyoung Jun, Jae-Han Lee, Chang-Su Kim
A typical monocular depth estimator is trained for a single camera, so its performance drops severely on images taken with different cameras.
1 code implementation • ECCV 2020 • Jae-Han Lee, Chang-Su Kim
To address these issues, we propose the loss rebalancing algorithm that initializes and rebalances the weight for each loss function adaptively in the course of training.